Earticle

초록

영어

Alternating Least Squares (ALS) is popular method to compute matrix factorization in the parallel way. However, due to the time complexity in predicting user’s preference, ALS is not scalable to large-scale datasets. In this paper, we propose a similar user index-based parallel matrix factorization approach. Since the group of similar users is indexed in advance, there is no need to compute similarities between all users in datasets. Furthermore, the size of a matrix is reduced because the matrix is only composed of indexed user’s ratings and items. The current advanced cloud computing including Hadoop, MapReduce and Amazon EC2 are employed to implement the proposed approaches. We empirically show that the use of similar user index resolves the scalable issue of ALS and improves the performance of large scale recommender systems in distributed computing environment.

목차

Abstract
 1. Introduction
 2. Background and Related Works
  2.1 Recommender Systems
  2.2 ALS
  2.3 Cloud Computing
 3. ALS-based Recommender System with the Use of Similar User Index
  3.1 Similar User Index
  3.2 Large Scale Implementation
 4. Evaluation
  4.1 Datasets
  4.2 Experimental Environment
  4.3 Results and Discussions
 5. Conclusion
 References

키워드

저자

• Haesung Lee [ Department of Computer Science, Kyonggi University ]
• Joonhee Kwon [ Department of Computer Science, Kyonggi University ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Software Engineering and Its Applications

• 간기

  월간

• pISSN

  1738-9984

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605